mindspore.ops.baddbmm

mindspore.ops.baddbmm(input, batch1, batch2, beta=1, alpha=1)

Perform a batch matrix-matrix product of matrices in batch1 and batch2 , input is added to the final result.

Note

• batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

• When batch1 is a $(C,W,T)$ tensor and batch2 is a $(C,T,H)$ tensor, input must be broadcastable with $(C,W,H)$ tensor, and out will be a $(C,W,H)$ tensor.

• If beta is 0, then input will be ignored.

• beta and alpha must be integers when inputs of type not FloatTensor.

$${\text{out}}_i=\beta {\text{input}}_i+\alpha ({\text{batch1}}_i@{\text{batch2}}_i)$$

Parameters

• input (Tensor) – The input tensor.

• batch1 (Tensor) – The first batch of matrices to be multiplied.

• batch2 (Tensor) – The second batch of matrices to be multiplied.

• beta (Union[float, int], optional) – Scale factor for input. Default 1 .

• alpha (Union[float, int], optional) – Scale factor for ( batch1 @ batch2 ). Default 1 .

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> input = mindspore.ops.ones((3, 3))
>>> batch1 = mindspore.tensor([[8., 7., 6.], [5., 4., 3.], [2., 1., 0.]])
>>> batch2 = mindspore.tensor([[5., 4., 3.], [2., 1., 0.], [8., 7., 6.]])
>>> output = mindspore.ops.baddbmm(input, batch1, batch2)
>>> print(output)
[[103.  82.  61.]
 [ 58.  46.  34.]
 [ 13.  10.   7.]]